mtmd: add batching API

common/arg.cpp

@@ -2243,6 +2243,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.image_max_tokens = value;
         }
     ).set_examples(mmproj_examples).set_env("LLAMA_ARG_IMAGE_MAX_TOKENS"));
+    add_opt(common_arg(
+        {"--mtmd-batch-max-tokens"}, "N",
+        string_format("maximum number of image tokens per batch when encoding images (default: %d)", params.mtmd_batch_max_tokens),
+        [](common_params & params, int value) {
+            params.mtmd_batch_max_tokens = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MTMD_BATCH_MAX_TOKENS"));
     if (llama_supports_rpc()) {
         add_opt(common_arg(
             {"--rpc"}, "SERVERS",

common/common.h

@@ -575,6 +575,7 @@ struct common_params {
     std::vector<std::string> image; // path to image file(s) ; TODO: change the name to "media"
     int image_min_tokens = -1;
     int image_max_tokens = -1;
+    int mtmd_batch_max_tokens = 1024;
 
     // finetune
     struct lr_opt lr;

tools/mtmd/clip-graph.h

@@ -54,6 +54,10 @@ struct clip_graph {
     virtual ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const;
     // TODO: build_mm(w, b, x) to support bias
 
+    virtual bool support_batch() const {
+        return false;
+    }
+
     //
     // utility functions
     //

tools/mtmd/clip.cpp

@@ -171,6 +171,8 @@ struct clip_ctx {
     std::map<ggml_backend_dev_t, size_t> mem_usage;
     std::map<ggml_backend_dev_t, size_t> mem_compute;
 
+    bool support_batch = false;
+
     clip_ctx(clip_context_params & ctx_params) {
         flash_attn_type = ctx_params.flash_attn_type;
         no_alloc = ctx_params.no_alloc;
@@ -862,7 +864,7 @@ ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale
     return cur;
 }
 
-static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
+static std::unique_ptr<clip_graph> clip_get_graph_builder(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
     const clip_image_f32 & img = *imgs.entries[0];
     std::unique_ptr<clip_graph> builder;
 
@@ -1025,7 +1027,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     // TODO [QWEN_VIDEO]: improve this in the future
     builder->n_batch = imgs.entries.size();
 
-    return builder->build();
+    return builder;
 }
 
 //
@@ -2819,7 +2821,7 @@ struct clip_model_loader {
         std::vector<support_info_op> ops;
     };
 
-    static void warmup(clip_ctx & ctx_clip) {
+    static clip_image_f32_batch get_dummy_batch(clip_ctx & ctx_clip) {
         // create a fake batch
         const auto & hparams = ctx_clip.model.hparams;
         clip_image_f32_batch batch;
@@ -2833,6 +2835,20 @@ struct clip_model_loader {
             LOG_INF("%s: warmup with audio size = %d\n", __func__, hparams.warmup_audio_size);
         }
         batch.entries.push_back(std::move(img));
+        return batch;
+    }
+
+    static void init_ctx(clip_ctx & ctx_clip) {
+        ctx_clip.buf_compute_meta.resize(ctx_clip.max_nodes * ggml_tensor_overhead() + ggml_graph_overhead());
+
+        // check batching support
+        auto batch = get_dummy_batch(ctx_clip);
+        auto builder = clip_get_graph_builder(&ctx_clip, batch);
+        ctx_clip.support_batch = builder->support_batch();
+    }
+
+    static void warmup(clip_ctx & ctx_clip) {
+        auto batch = get_dummy_batch(ctx_clip);
         warmup(ctx_clip, batch);
     }
 
@@ -2905,9 +2921,7 @@ struct clip_model_loader {
 
     // only initialize backend buffers, but do not allocate them yet
     static support_info_graph reserve_compute_meta(clip_ctx & ctx_clip, const clip_image_f32_batch & batch) {
-        ctx_clip.buf_compute_meta.resize(ctx_clip.max_nodes * ggml_tensor_overhead() + ggml_graph_overhead());
-
-        ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch);
+        ggml_cgraph * gf = clip_get_graph_builder(&ctx_clip, batch)->build();
         ggml_backend_sched_reserve(ctx_clip.sched.get(), gf);
 
         ctx_clip.mem_compute.clear();
@@ -3070,6 +3084,7 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
             ctx_vision = new clip_ctx(ctx_params);
             loader.load_hparams(ctx_vision->model, CLIP_MODALITY_VISION);
             loader.load_tensors(*ctx_vision);
+            loader.init_ctx(*ctx_vision);
             if (ctx_params.warmup) {
                 loader.warmup(*ctx_vision);
             }
@@ -3083,6 +3098,7 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
             ctx_audio = new clip_ctx(ctx_params);
             loader.load_hparams(ctx_audio->model, CLIP_MODALITY_AUDIO);
             loader.load_tensors(*ctx_audio);
+            loader.init_ctx(*ctx_audio);
             if (ctx_params.warmup) {
                 loader.warmup(*ctx_audio);
             }
@@ -3484,25 +3500,22 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
     return n_patches;
 }
 
-bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
+bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, std::vector<float> & out_vec) {
     clip_image_f32_batch imgs;
     clip_image_f32_ptr img_copy(clip_image_f32_init());
     *img_copy = *img;
     imgs.entries.push_back(std::move(img_copy));
 
-    return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
+    return clip_image_batch_encode(ctx, n_threads, &imgs, out_vec);
 }
 
-bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs_c_ptr, float * vec) {
+bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs_c_ptr, std::vector<float> & out_batch_embd) {
     const clip_image_f32_batch & imgs = *imgs_c_ptr;
     int n_batch_cur = imgs.entries.size();
 
-    // maximum supported batch size, usually == 2 for qwen-vl-based models
-    int n_batch_max = clip_model_n_batch_max(ctx);
-
-    // TODO @ngxson : implement batch size > 1 as a loop
-    //                we don't need true batching support because the cgraph will gonna be big anyway
-    if (n_batch_cur > n_batch_max) {
+    // [QWEN_VIDEO] for video models, the batch dimension is used as temporal dimension for merged frames
+    if (!ctx->support_batch && n_batch_cur > clip_model_n_temporal_merge(ctx)) {
+        LOG_ERR("%s: batch size %d exceeds maximum supported batch/temporal-merge size %d\n", __func__, n_batch_cur, clip_model_n_temporal_merge(ctx));
         return false;
     }
 
@@ -3513,7 +3526,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
 
     // build the inference graph
     ggml_backend_sched_reset(ctx->sched.get());
-    ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
+    ggml_cgraph * gf = clip_get_graph_builder(ctx, imgs)->build();
     ggml_backend_sched_alloc_graph(ctx->sched.get(), gf);
 
     // set inputs
@@ -3582,6 +3595,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             const int n  = nx * ny;
 
             for (int b = 0; b < n_batch_cur; b++) {
+                LOG_DBG("%s: copying image %d/%d to input buffer (nx=%d, ny=%d)\n", __func__, b+1, n_batch_cur, nx, ny);
                 const auto & buf = imgs.entries[b]->get_ro_buf();
                 float * batch_entry = inp_raw.data() + b * (3*n);
                 for (int y = 0; y < ny; y++) {
@@ -4416,24 +4430,34 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     // the last node is the embedding tensor
     ggml_tensor * embeddings = ggml_graph_node(gf, -1);
 
-    // sanity check (only support batch size of 1 for now)
+    // sanity check (assuming that all images in batch have the same number of tokens, so we only check the first one)
     const int n_tokens_out = embeddings->ne[1];
     const int expected_n_tokens_out = clip_n_output_tokens(ctx, imgs.entries[0].get());
     if (n_tokens_out != expected_n_tokens_out) {
         LOG_ERR("%s: expected output %d tokens, got %d\n", __func__, expected_n_tokens_out, n_tokens_out);
         GGML_ABORT("Invalid number of output tokens");
     }
 
-    // copy the embeddings to the location passed by the user
-    if (vec != nullptr) {
-        ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
+    LOG_DBG("%s: output embedding shape [%d, %d, %d]\n", __func__,
+        (int)embeddings->ne[0], (int)embeddings->ne[1], (int)embeddings->ne[2]);
+
+    // copy output to user buffer if provided
+    // if output is empty, skip the copy
+    if (!out_batch_embd.empty()) {
+        if (out_batch_embd.size() != (size_t)ggml_nelements(embeddings)) {
+            LOG_ERR("%s: output buffer has %zu elements but expected %zu\n", __func__, out_batch_embd.size(), (size_t)ggml_nelements(embeddings));
+            GGML_ABORT("Output buffer size mismatch");
+        }
+        ggml_backend_tensor_get(embeddings, out_batch_embd.data(), 0, ggml_nbytes(embeddings));
+    } else {
+        LOG_WRN("%s: output buffer is empty, skipping copy\n", __func__);
     }
 
     // Debug: dump final embeddings if MTMD_DEBUG_EMBEDDINGS is set
     if (ctx->debug_output_embeddings) {
         const int64_t n_embd = embeddings->ne[0];
         const int64_t n_tokens = embeddings->ne[1];
-        std::vector<float> emb_data(n_embd * n_tokens);
+        std::vector<float> emb_data(ggml_nelements(embeddings));
         ggml_backend_tensor_get(embeddings, emb_data.data(), 0, ggml_nbytes(embeddings));
 
         LOG_INF("\n=== MTMD_DEBUG_EMBEDDINGS ===\n");
@@ -4570,7 +4594,14 @@ bool clip_has_audio_encoder(const struct clip_ctx * ctx) {
     return ctx->model.modality == CLIP_MODALITY_AUDIO;
 }
 
-int clip_model_n_batch_max(const struct clip_ctx * ctx) {
+bool clip_support_batch(const struct clip_ctx * ctx) {
+    return ctx->support_batch;
+}
+
+// TODO @ngxson : this is no longer correct with mtmd_batch API
+// this was only meant to be used by qwen-vl-based models, to fuse 2 input images into one (qwen-vl video support)
+// this logic should be refactored in near future to distinctly handle "merge frames" and "batching"
+int clip_model_n_temporal_merge(const struct clip_ctx * ctx) {
     switch (ctx->proj_type()) {
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:

tools/mtmd/clip.h

@@ -97,8 +97,8 @@ size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int id
 size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
 struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
 
-bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
-bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
+bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, std::vector<float> & out_vec);
+bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, std::vector<float> & out_batch_embd);
 
 bool clip_is_llava(const struct clip_ctx * ctx);
 // note for contributor: this clip_is_(model) pattern is deprecated
@@ -107,7 +107,9 @@ bool clip_is_llava(const struct clip_ctx * ctx);
 bool clip_has_vision_encoder(const struct clip_ctx * ctx);
 bool clip_has_audio_encoder(const struct clip_ctx * ctx);
 
-int clip_model_n_batch_max(const struct clip_ctx * ctx);
+bool clip_support_batch(const struct clip_ctx * ctx);
+
+int clip_model_n_temporal_merge(const struct clip_ctx * ctx); // TODO @ngxson : remove, refactor this
 
 std::map<ggml_backend_dev_t, size_t> clip_get_mem_usage(const struct clip_ctx * ctx);
 

tools/mtmd/models/gemma4v.cpp

@@ -10,7 +10,7 @@ ggml_cgraph * clip_graph_gemma4v::build() {
     ggml_set_name(inp_raw, "inp_raw_scaled");
 
     ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
-    inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd);
+    inp = ggml_reshape_3d(ctx0, inp, n_patches, n_embd, n_batch);
     inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
     ggml_set_name(inp, "inp");
     // note: no patch bias
@@ -51,10 +51,11 @@ ggml_cgraph * clip_graph_gemma4v::build() {
         // first half
         ggml_tensor * first;
         {
-            first = ggml_view_3d(ctx0, cur,
-                n_dim/2, n_head, n_pos,
+            first = ggml_view_4d(ctx0, cur,
+                n_dim/2, n_head, n_pos, n_batch,
                 cur->nb[1],
                 cur->nb[2],
+                cur->nb[3],
                 0);
             first = ggml_rope_ext(
                 ctx0,
@@ -70,10 +71,11 @@ ggml_cgraph * clip_graph_gemma4v::build() {
         // second half
         ggml_tensor * second;
         {
-            second = ggml_view_3d(ctx0, cur,
-                n_dim/2, n_head, n_pos,
+            second = ggml_view_4d(ctx0, cur,
+                n_dim/2, n_head, n_pos, n_batch,
                 cur->nb[1],
                 cur->nb[2],
+                cur->nb[3],
                 n_dim/2 * ggml_element_size(cur));
             second = ggml_rope_ext(
                 ctx0,
@@ -103,14 +105,14 @@ ggml_cgraph * clip_graph_gemma4v::build() {
         const int kernel_size = hparams.n_merge;
         GGML_ASSERT(kernel_size > 0);
 
-        // [n_embd, n_patches] -> [n_patches_x, n_patches_y, n_embd, 1]
-        cur = ggml_cont_4d(ctx0, ggml_transpose(ctx0, cur), n_patches_x, n_patches_y, n_embd, 1);
+        // [n_embd, n_patches] -> [n_patches_x, n_patches_y, n_embd, n_batch]
+        cur = ggml_cont_4d(ctx0, ggml_transpose(ctx0, cur), n_patches_x, n_patches_y, n_embd, n_batch);
         cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG,
                            kernel_size, kernel_size, kernel_size, kernel_size, 0, 0);
         const int out_x = n_patches_x / kernel_size;
         const int out_y = n_patches_y / kernel_size;
-        // [out_x, out_y, n_embd, 1] -> [n_embd, out_x * out_y]
-        cur = ggml_reshape_3d(ctx0, cur, out_x * out_y, n_embd, 1);
+        // [out_x, out_y, n_embd, n_batch] -> [n_embd, out_x * out_y, n_batch]
+        cur = ggml_reshape_3d(ctx0, cur, out_x * out_y, n_embd, n_batch);
         cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
         cur = ggml_scale(ctx0, cur, sqrtf((float)n_embd));
         cb(cur, "pooled", -1);

tools/mtmd/models/models.h

@@ -16,6 +16,7 @@ struct clip_graph_gemma4v : clip_graph {
     clip_graph_gemma4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
     ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
+    bool support_batch() const override { return true; }
 };
 
 struct clip_graph_gemma4uv : clip_graph {

tools/mtmd/mtmd-helper.h

@@ -67,8 +67,8 @@ MTMD_API void mtmd_helper_image_get_decoder_pos(const mtmd_image_tokens * image,
 
 // helper function that automatically:
 // 1. run llama_decode() on text chunks
-// 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode()
-// if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error
+// 2. run mtmd_encode_chunk() on image chunks, then mtmd_get_output_embd() and then llama_decode()
+// if any of the mtmd_encode_chunk() or llama_decode() calls return non-zero, stop and forward the error
 // otherwise, returns 0 on success
 // this function is NOT thread-safe
 MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
@@ -157,13 +157,16 @@ MTMD_API int32_t mtmd_helper_video_read_next(mtmd_helper_video * ctx,
 } // extern "C"
 #endif
 
+#ifdef __cplusplus
+#include <set>
+#include <memory>
+
+namespace mtmd_helper {
+
 //
 // C++ wrappers
 //
 
-#ifdef __cplusplus
-namespace mtmd_helper {
-
 // video-related C++ wrappers
 struct mtmd_helper_video_deleter {
     void operator()(mtmd_helper_video * val) { mtmd_helper_video_free(val); }